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Journal articles on the topic 'ANN CONTROLLER'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Banda, Gururaj, and Sri Gowri Kolli. "An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System." World Electric Vehicle Journal 12, no. 1 (2021): 44. http://dx.doi.org/10.3390/wevj12010044.

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This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stabilit

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Alatshan, Mohammed Salheen, Ibrahim Alhamrouni, Tole Sutikno, and Awang Jusoh. "Improvement of the performance of STATCOM in terms of voltage profile using ANN controller." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (2020): 1966. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp1966-1978.

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The electronic equipments are extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed

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Mohammed, Salheen Alatshan, AlHamrouni Ibrahim, Sutikno Tole, and Jusoh Awang. "Improvement of the performance of STATCOM in terms of voltage profile using ANN controller." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 4 (2020): 1966–78. https://doi.org/10.11591/ijpeds.v11.i4.pp1966-1978.

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The electronic equipments are extremely sensitive to variation in electric supply. The increasing of a nonlinear system with several interconnected unpredicted and non-linear loads are causing some problems to the power system. The major problem facing the power system is power quality, controlling of reactive power and voltage drop. A static synchronous compensator (STATCOM) is an important device commonly used for compensation purposes, it can provide reactive support to a bus to compensate voltage level. In this paper, the Artificial Neural Network (ANN) controlled STATCOM has been designed

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Chen, Wei Lun, and Gong Cai Xin. "Research on ANN Dynamic Inversion Control of UAV." Advanced Materials Research 466-467 (February 2012): 1353–57. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1353.

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The paper proposes a method to design AANN dynamic inversion controller through online ANN compensating inversion error. It mainly aims at evident shortage of dynamic inversion controller of UAV. A single hidden layer ANN structure is constructed and the stability of the whole closed loop system is proved. Also the stable adjustment arithmetic of online ANN weight is proposed. The robustness, the adaptability to fault and the response capability to actuator delay time of the scheme are verified by simulation. It is also proved that the online ANN has improved the performance of dynamic inversi

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Lee, Heung-Jae, Seong-Su Jhang, Won-Kun Yu, and Jung-Hyun Oh. "Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems." Energies 12, no. 17 (2019): 3372. http://dx.doi.org/10.3390/en12173372.

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This paper proposed an ANN (Artificial Neural Network) controller to damp out inter-area oscillation of a power system using BESS (Battery Energy Storage System). The conventional lead-lag controller-based PSSs (Power System Stabilizer) have been designed using linear models usually linearized at heavy load conditions. This paper proposes a non-linear ANN based BESS controller as the ANN can emulate nonlinear dynamics. To prove the performance of this nonlinear PSS, two linear PSS are introduced at first which are linearized at the heavy load and light load conditions, respectively. It is then

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Mugheri, N. H., M. U. Keerio, S. Chandio, and R. H. Memon. "Robust Speed Control of a Three Phase Induction Motor Using Support Vector Regression." Engineering, Technology & Applied Science Research 11, no. 6 (2021): 7861–66. http://dx.doi.org/10.48084/etasr.4476.

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The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mat

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Albert Alexander, S., R. Harish, M. Srinivasan, and D. Sarathkumar. "Power Quality Improvement in a Solar PV Assisted Microgrid Using Upgraded ANN-Based Controller." Mathematical Problems in Engineering 2022 (October 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/2441534.

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This paper proposes the design of a controller using the artificial neural network (ANN) for a solar photovoltaic (PV)-fed cascaded multilevel inverter (CMLI) to enhance the power quality. The objective of this presented ANN controller is to obtain a maximum output voltage with no filter components. This paper also investigates and eliminates the voltage harmonics that occurred in a solar-fed cascaded 3-stage inverter using various techniques such as pulse width modulation (PWM), digital logic control (DLC), fuzzy logic controller (FLC), and ANN, and the results are compared. Based on the resu

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Mani Nepal, Subhamyu, Madan Prasad Sapkota, Nischal Shrestha, Sunil Karki, and Manoj Raj Dhungana. "Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control." KEC Journal of Science and Engineering 8, no. 1 (2024): 44–47. http://dx.doi.org/10.3126/kjse.v8i1.69264.

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DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.

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Liu, Bao, Na Gao, Fei Liu, Ling Fan, and Yi Yong Sui. "An Improved ANN Controller on the Efficiency Optimization of Offshore Petroleum Platform." Applied Mechanics and Materials 571-572 (June 2014): 1042–46. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.1042.

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An improved ANN controller is presented inspired from hormone modulation function. This ANN controller consists of the main ANN controller and the conventional controller. To increase the learning efficiency, the slop of the excitation function is changed by the correcting parameters according to the hormone modulation law. To improve the control accuracy, we chose the accumulation of control error during the regulating process. And to avoid the integrated saturation, we judge the input of BP based on the absolute value of error. The main ANN controller adjusts the control input of the seconda

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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dim

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Jarupula, Somlal, Narsimha Rao Vutlapalli, and Narsimha Rao Vutlapalli. "Power Quality Improvement in Distribution System using ANN Based Shunt Active Power Filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 5, no. 4 (2015): 568. http://dx.doi.org/10.11591/ijpeds.v5.i4.pp568-575.

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<p>This paper focuses on an Artificial Neural Network (ANN) controller based Shunt Active Power Filter (SAPF) for mitigating the harmonics of the distribution system. To increase the performance of the conventional controller and take advantage of smart controllers, a feed forward-type (trained by a back propagation algorithm) ANN-based technique is implemented in shunt active power filters for producing the controlled pulses required for IGBT inverter. The proposed approach mainly work on the principle of capacitor energy to maintain the DC link voltage of a shunt connected filter and t

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Jagtap, Prashant, and V. K. Chandrakar. "Intelligent method for Power flow using Artificial Neural Network with UPFC." Journal of Physics: Conference Series 2763, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2763/1/012004.

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Abstract It is slightly difficult to transmit such a large amount of electricity in very flexible and efficient way as the size of conventionally used relay and protective devices will increase while transmitting such a large amount of electricity which will increase the operating cost. So for transmission of such a large amount of electricity, Flexible Alternating Current Transmission Systems (FACTS) will have been found to be promising aspect in terms of the analysis of the power system. In this paper the comparative analysis of unified power flow controller(upfc) is presented in presence of

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Mahar, Hina, Hafiz Mudasir Munir, Jahangir Badar Soomro, et al. "Implementation of ANN Controller Based UPQC Integrated with Microgrid." Mathematics 10, no. 12 (2022): 1989. http://dx.doi.org/10.3390/math10121989.

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This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-b

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Bahamani, Akhib Khan, G. M. Sreerama Reddy, and V. Ganesh. "Power Quality Improvement in Fourteen Bus System using Non-Conventional Source Based ANN Controlled DPFC System." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (2016): 499. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp499-507.

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DPFC can be used to improve receiving end voltage of fourteen bus system. This paper shows the conception and simulation of wind and solar based distribution power flow controller for sag compensation and ohmic loss reduction. The objectives of this work are to improve the voltage and reduce the line losses. Fourteen bus systems with DPFC in open loop is simulated. Fourteen bus system with DPFC in closed loop using PI and ANN are also simulated and the results are presented. The comparative study is presented to demonstrate the improvement in dynamic response of ANN controlled DPFC system. ANN

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Hernandez-Lopez, Ybrain, Raul Rivas-Perez, and Vicente Feliu-Batlle. "Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool." Applied Sciences 12, no. 18 (2022): 9180. http://dx.doi.org/10.3390/app12189180.

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The management of irrigation main canals are studied in this research. One way of improving this is designing an efficient automatic control system of the water that flows through the canal pools, which is usually carried out by PI controllers. However, since canal pools are systems with large time delays and nonlinear hydrodynamics, these PIs are tuned in a very conservative way so that the closed-loop instability that may appear depending on the chosen operation regime is avoided. These controllers are inefficient because they have slow time responses. In order to obtain faster responses tha

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Somwong, Poom, Karn Patanukhom, and Yuthapong Somchit. "Energy-Aware Controller Load Distribution in Software-Defined Networking using Unsupervised Artificial Neural Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 289–314. https://doi.org/10.58346/jowua.2025.i1.018.

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Software-Defined Networking (SDN) enhances network management by separating the control and data planes into controllers and switches, allowing for centralized, programmable networks with multiple controllers. Switches are mapped to controllers and exchange control messages to manage the network, which leads to significant energy consumption. Managing energy in networks has become a critical issue, as dynamic changes in switch loads can cause controller overloads, necessitating the migration of switches to other controllers. As networks grow, energy consumed in control communications becomes a

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A, Mr Aneerudh. "Design and Analysis of ANN Control based LLC Resonant Converter." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 73–80. http://dx.doi.org/10.22214/ijraset.2023.48952.

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Abstract: In this, artificial neural network controller is designed for LLC resonant converter for voltage regulation. The performance of the proposed converter with proportional-integral (PI) controller and ANN controller are analysed from the simulation results. A voltage mode control is provided to get regulated load voltage irrespective of the changes in supply. ANN controller is used for the voltage mode control and the efficiency of the proposed ANN controller is estimated and comparison is made with conventional PI controller. The simulation work is done with MATLAB/Simulink software

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Shreyanth, S. "Quadcopter based Robotic Injection Lubricator for High Altitude Mechanical Structures employing Image Processing based Ensemble Machine Learning." International Research Journal of Engineering and Management Studies (IRJEMS) 3, no. 5 (2019): 15–24. https://doi.org/10.5281/zenodo.8047977.

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To govern dynamical systems online, an adaptive artificial neural network (ANN)-based proportional integral derivative (PID) controller is designed. The traditional PID controller is employed for linear time invariant processes, however it has drawbacks when controlling plants with significant nonlinearity or whose parameters change over time. The related information about the plant's dynamics is required to determine the parameters of PID controllers. If plant parameters are perturbed, the PID controller may function if the changes are not severe. However, most plants are either non-linea

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Alhalim, Shaimaa Shukri Abd, Wissem Bahloul, Mohamed Chtourou, and Nabil Derbel. "A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18459–68. https://doi.org/10.48084/etasr.8537.

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This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a P

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BENCHIKH, Salma, Tarik JAROU, Mohamed Khalifa BOUTAHIR, Elmehdi NASRI, and Roa ELAMRANI. "Improving Photovoltaic System Performance with Artificial Neural Network Control." Data and Metadata 2 (December 30, 2023): 144. http://dx.doi.org/10.56294/dm2023144.

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Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels

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Boudjellal, Bilal, and Tarak Benslimane. "Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller." Modelling, Measurement and Control A 93, no. 1-4 (2020): 31–38. http://dx.doi.org/10.18280/mmc_a.931-405.

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The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maxi

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Mohammad, Ahmed Ibrahim, Saleh Alhfidh Ali, and Nathem Hamoodi Ali. "Performance enhancement of small-scale wind turbine based on artificial neural network." International Journal of Power Electronics and Drive Systems 14, no. 3 (2023): 1722~1730. https://doi.org/10.11591/ijpeds.v14.i3.pp1722-1730.

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Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work represent the relationship between

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Setio, Herlien Dwiarti, Pei-ching Chen, Sangriyadi Setio, Michael Felix Sinjaya, and Cecilia Andriana. "Numerical and Experimental Study of Seismically Excited Scaled Structure with Active Mass Damper." Journal of Engineering and Technological Sciences 56, no. 5 (2024): 613–24. http://dx.doi.org/10.5614/j.eng.technol.sci.2024.56.5.6.

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In recent years, the development and implementation of artificial intelligence (AI) have attracted tremendous attention. The implementation of active control systems for building structures can be improved by using an AI controller. Non-AI controllers such as the Linear Quadratic Regulator (LQR) controller require full state variables of the structure to be measured, which is rarely feasible. To address this problem, two AI models, namely, artificial neural network (ANN) and fuzzy logic (FL), have been tried as AI-based controller in various studies. In the present study, both AI models were i

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Meguellati, Mourad, Mohammed Salah Khireddine, and Kheireddine Chafaa. "Comparative Study of PID and ANN Controllers for AC Output Voltage Regulation in a Photovoltaic Grid." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23290–98. https://doi.org/10.48084/etasr.10904.

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The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpos

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Sim, S. Y., C. K. Chia, W. M. Utomo, et al. "Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 105. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp105-112.

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This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards t

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S., Y. Sim, K. Chia C., M. Utomo W., et al. "Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 105–12. https://doi.org/10.11591/ijeecs.v11.i1.pp105-112.

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This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards t

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Mohamed, Asghaiyer Omran, I. Ibrahim Izzeldin., Zaharin Ahmad Abu, et al. "Comparisons of PI and ANN controllers for shunt HPF based on STF-PQ Algorithm under distorted grid voltage." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 3 (2019): 1339–46. https://doi.org/10.11591/ijpeds.v10.i3.pp1339-1346.

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This paper proposes a shunt hybrid power filter (HPF) for harmonic currents and reactive power compensation under a distorted voltage and in a polluted environment. For this purpose, the reference current of the shunt HPF is computed based on the instantaneous reactive power (p-q) theory with selftuning filter (STF). In order to adjust the dc voltage as a reference value, PI and ANN controllers have been utilized. Moreover, the system has been implemented and simulated in a MATLAB-SIMULINK platform, and selected results are presented. Therefore, the results verified the good dynamic performanc

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Mohamad, Milood Almelian, I. Mohd Izzeldin., Zaharin Ahmad Abu, et al. "Enhancing the performance of cascaded three-level VSC STATCOM by ANN controller with SVPWM integegration." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3880–90. https://doi.org/10.11591/ijece.v9i5.pp3880-3890.

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This article presents a cascaded three-level voltage source converter (VSC) based STATCOM employing an artificial neuron network (ANN) controller with a new simple circuit of space vector pulse width modulation (SVPWM) technique. The main aim of utilizing ANN controller and SVPWM technique is to minimize response time (RT) of STATCOM and improve its performance regard to PF amplitude, and total harmonic distortion (THD) of VSC output current during the period of lagging/leading PF loads (inductive/capacitive loads). The performance of STATCOM is tested using MATLAB/SIMULINK in IEEE 3-bus syste

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Bodapati, Rakesh Babu, R. S. Srinivas, and P. V. Ramana Rao. "Artificial Neural Network-Based Hybrid Controller for Electric Vehicle Applications." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 23 (October 31, 2024): 192–201. http://dx.doi.org/10.37394/23201.2024.23.20.

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Power management among different energy sources of electric vehicles (EV) is one of the complex issues during the transition from one to another. A specific control is modeled based on the current and speed range of the electric motor named as Measurement of Parameter-Based Controller (MPBC), which will play a key role during transition of energy sources as per the load requirement. Two bidirectional converters are utilized to control the pulse signals generated by the traditional controllers which are connected at the battery and Supercapacitors (SCap) ends, which are treated as passive sourc

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Chen, Guoshao, and Zhiping Liu. "Artificial Neural Network-Based Feed-Forward and Feedback Control Design and Convergence Analysis." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/1238020.

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A feed-forward and feedback control scheme based on artificial neural network (ANN) and iterative learning control is proposed. Iterative learning control and ANN are combined as a feed-forward controller, which makes the output track the desired trajectory. Feedback control is introduced to reduce the effect of disturbances. To combine the feed-forward controller and the feedback controller, the ANN is employed to simulate the plant. Since the ANN can update the weights online, it is always consistent with the plant. The convergence and robustness of the system are analyzed, and the simulatio

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Ahmed, Shouket A., Abadal Salam T. Hussain, F. Malek, et al. "Intelligent Controller of High Voltage Power Station Based Artificial Neural Network." Applied Mechanics and Materials 793 (September 2015): 505–9. http://dx.doi.org/10.4028/www.scientific.net/amm.793.505.

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In this papermulti-layer perceptron (MLP) artificial neural networks (ANN) theory is presented as an efficient controllerfor the high voltage direct current (HVDC) power station systems. The results demonstrated successful performance for single mode control using an MLP-ANN based on-line power controller. The main advantage by using ANN controllers such as optimal control system over a wide operating range, which is a capable of on-line adaptation makes the power systems no a prior knowledge and has a huge database with capacity to learn from previous experience.

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Idrissi, Yassine El Aidi, Khalid Assalaou, Lahoussine Elmahni, and Elmostafa Aitiaz. "New improved MPPT based on artificial neural network and PI controller for photovoltaic applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1791. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1791-1801.

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This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controll

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Yassine, El Aidi Idriss, Assalaou Khalid, Elmahni Lahoussine, and Aitiaz Elmostafa. "New improved MPPT based on artificial neural network and PI controller for photovoltaic applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (2022): 1791–801. https://doi.org/10.11591/ijpeds.v13.i3.pp1791-1801.

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This paper details an maximum power point tracking (MPPT) approach based on artificial neural network (ANN) to track the maximum power produced by a PV panel. This approach is rapid and accurate for following the maximum power point (MPP) during changes in weather conditions such as solar irradiation and temperature. A PV system structure including an MPPT controller is studied, designed, and simulated in this work. The aim of this paper is to use the artificial neural network (ANN) technique to develop a MPPT controller for PV applications. To increase the performance of the ANN-MPPT controll

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Giri, Surya Prakash, and Sunil Kumar Sinha. "Four-Area Load Frequency Control of an Interconnected Power System Using Neuro-Fuzzy Hybrid Intelligent Proportional and Integral Control Approach." Journal of Intelligent Systems 22, no. 2 (2013): 131–53. http://dx.doi.org/10.1515/jisys-2012-0025.

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AbstractThis article presents a novel control approach, hybrid neuro-fuzzy (HNF), for the load frequency control (LFC) of a four-area interconnected power system. The advantage of this controller is that it can handle nonlinearities, and at the same time, it is faster than other existing controllers. The effectiveness of the proposed controller in increasing the damping of local and inter-area modes of oscillation is demonstrated in a four-area interconnected power system. Areas 1 and 2 consist of a thermal reheat power plant, whereas Areas 3 and 4 consist of a hydropower plant. Performance ev

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Meena, Devi R., and Premalatha L. "Efficient figureconverter fed PMBLDC motor using artificial neural network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 3025–31. https://doi.org/10.11591/ijece.v9i4.pp3025-3031.

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In this paper, a new design of Bridgeless SEPIC (Single Ended Primary Inductance converter) with Artificial neural network (ANN) fed PMBLDC Motor drive is proposed to improve Power Factor. The proposed converter has single switching device of MOSFET, so the switching losses is reduced. ANN is used to achieve the higher power factor and fixed dc link voltage. Also the ANN methodology the time taken for computation is less since there is no mathematical model. The output voltage depends on the switching frequency of the MOSFET. The BLSEPIC act as a buck operation in continuous conduction mode. D

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Gounder, Yasoda Kailasa, and Sowkarthika Subramanian. "Application of machine learning controller in matrix converter based on model predictive control algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1489. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1489-1496.

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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The t

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Purushotham, K. "Design and Implementation of Electric Vehicle Technology by Using ANN Controller." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1376–87. http://dx.doi.org/10.22214/ijraset.2022.40065.

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Abstract: It is possible to utilise EVs as both a load and provider of energy using the Vehicle-to-Grid (V2G) approach (or Gridto-Vehicle technique if EVs are used as a load). With this technology, industrial microgrids may have voltage and power flow regulation and congestion management. An no of electric vehicles with a variety of charging profiles, battery states of charge and electric vehicle counts may benefit from two separate controllers (grid regulation and charger controller), according to the controllers, It is possible to regulate the main power flow and voltage drop in an industria

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Pajchrowski, Tomasz, Konrad Urbański, and Krzysztof Zawirski. "Artificial neural network based robust speed control of permanent magnet synchronous motors." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 25, no. 1 (2006): 220–34. http://dx.doi.org/10.1108/03321640610634461.

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PurposeThe aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which repr

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Bakou, Youcef, Mohamed Abid, Lakhdar Saihi, Abdel Ghani Aissaoui, and Youcef Hammaoui. "Hybrid sliding neural network controller of a direct driven vertical axis wind turbine." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 10–20. http://dx.doi.org/10.11591/eei.v12i1.4214.

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This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) scheme for controlling the stator power (active/reactive) of a doubly fed induction generator (DFIG)-based direct drive vertical axis wind turbine (VAWT) power system under a real-world scenario wind speed that will be installed in the Adrar region (Saharan zone) of Algeria. The SM-ANN scheme will control the stator power of the direct drive VAWT power. The chattering phenomenon is the most significant disadvantage associated with sliding mode control (SMC). In order to find a solution to this is

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Katuri, Raghavaiah, and Srinivasa Rao Gorantla. "Design and Analysis of Math Function Based Controller Combined with Fuzzy Logic Applied to the Solar-Powered Electric Vehicle." Fronteiras: Journal of Social, Technological and Environmental Science 11, no. 1 (2022): 315–32. http://dx.doi.org/10.21664/2238-8869.2022v11i1.p315-332.

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The transition between the battery and ultracapacitor (UC) according to the driver requirements is the key obstacle that is related to the hybrid energy storage system (HESS) powered electric vehicles (EVs). In this effort, an innovative control scheme has been proposed, to switch the power sources corresponding to the vehicle dynamics. An MFB controller is considered with 4- math functions and programmed independently. Thereafter, a new hybrid controller has been formed by joining the designed MFB controller with an artificial neural network (ANN) to achieve the precise transition between bat

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Faiz, Aunowar Mohammad, and Jacqueline Lukose. "Optimization of Series Compensation in Transmission Networks Using Artificial Neural Networks." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3443–54. http://dx.doi.org/10.1166/jctn.2019.8306.

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To respond to the ever-increasing power demand of load centers, power is transmitted at extrahigh voltages. However, an increase in power transfer level should be supported by an enhanced level of security. Flexible AC Transmission System (FACTS) devices present an economical and efficient alternative to consider for achieving higher power transfer level with enhanced security instead of introducing new transmission facilities, to maintain a large stability margin of power in transmission line. This project aims to optimize the level of series compensation in transmission networks using Artifi

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Omran, Mohamed Asghaiyer, Izzeldin I. Ibrahim, Abu Zaharin Ahmad, et al. "Comparisons of PI and ANN controllers for shunt HPF based on STF-PQ Algorithm under distorted grid voltage." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 3 (2019): 1339. http://dx.doi.org/10.11591/ijpeds.v10.i3.pp1339-1346.

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<span>This paper proposes a shunt hybrid power filter (HPF) for harmonic currents and reactive power compensation under a distorted voltage and in a polluted environment. For this purpose, the reference current of the shunt HPF is computed based on the instantaneous reactive power (p-q) theory with self-tuning filter (STF). In order to adjust the dc voltage as a reference value, PI and ANN controllers have been utilized. Moreover, the system has been implemented and simulated in a MATLAB-SIMULINK platform, and selected results are presented. Therefore, the results verified the good dynam

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Ibrahim, Mohammad Ahmed, Ali Saleh Saleh, and Ali Nathem Hamoody. "Performance enhancement of small-scale wind turbine based on artificial neural network." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1722. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1722-1730.

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<p><span lang="EN-US">Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work

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Venkata Anjani Kumar G and M. Damodar Reddy. "Mitigation of Grid Current Harmonics by ABC- ANN based Shunt Active Power Filter." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 285–98. http://dx.doi.org/10.37934/araset.33.1.285298.

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Harmonics are being introduced into power system networks as a result of the increasing use of nonlinear devices. These harmonics cause distortion of current and voltage signals, which in turn causes damage to power distribution systems. As a result, the suppression of harmonics is of extreme significance in power systems. This paper proposes Shunt Active Power Filters (SAPF) based on neural network algorithms like Artificial Neural Network (ANN) as a feasible approach to mitigating harmonic distortion and raising power quality in electrical distribution systems. This research shows that using

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G, Venkata Anjani kumar, and Damodar Reddy M. "TLBO-trained ANN-based Shunt Active Power Filter for Mitigation of Current Harmonics." International Journal of Experimental Research and Review 34, Special Vo (2023): 11–21. http://dx.doi.org/10.52756/ijerr.2023.v34spl.002.

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The increased utilization of nonlinear devices is resulting in damage to power distribution infrastructure by introducing harmonics into power system networks, which in turn causes distortion in voltage and current signals. A novel solution called Shunt Active Power Filter (SAPF) has been developed to address this issue using power electronics. This study aims to provide a method that is efficient and cost-effective for lowering harmonics and improving power quality in distribution infrastructure. The proposed method combines the Teaching learning-based optimization (TLBO) technique with an Ar

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Khomenko, Veligorskyi, Chakirov, and Vagapov. "An ANN-Based Temperature Controller for a Plastic Injection Moulding System." Electronics 8, no. 11 (2019): 1272. http://dx.doi.org/10.3390/electronics8111272.

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This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and fast

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Rezaee, Mehdi, and Yusuf Gürcan Şahin. "An Overview of ANN based MPPT and an Example." International Journal of Engineering Technologies IJET 10, no. 1 (2025): 9–22. https://doi.org/10.19072/ijet.1716330.

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The study presents an overview and a simulation of maximum power point tracking (MPPT) for Photovoltaic (PV) systems that uses an artificial neural network (ANN) controller as proof of concept. Solar energy must be harvested with high efficien-cy as the world turns to renewables. The usual Perturb and Observe (P&amp;O) and Incremental (InC) method loses power by oscil-lating around the Maximum Power Point (MPP) and reacts slowly to sudden weather changes. The work therefore tests an ANN as a better choice. The authors survey earlier ANN MPPT studies that cover many network types, training

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Moore, Jared M., and Philip K. McKinley. "Evolution of Joint-Level Control for Quadrupedal Locomotion." Artificial Life 23, no. 1 (2017): 58–79. http://dx.doi.org/10.1162/artl_a_00222.

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We investigate a hierarchical approach to robot control inspired by joint-level control in animals. The method combines a high-level controller, consisting of an artificial neural network (ANN), with joint-level controllers based on digital muscles. In the digital muscle model (DMM), morphological and control aspects of joints evolve concurrently, emulating the musculoskeletal system of natural organisms. We introduce and compare different approaches for connecting outputs of the ANN to DMM-based joints. We also compare the performance of evolved animats with ANN-DMM controllers with those gov

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Basavarajappa, Sokke Rameshappa, and Mudakapla Shadaksharappa Nagaraj. "An optimal artificial neural network controller for load frequency control of a four-area interconnected power system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4700–4711. https://doi.org/10.11591/ijece.v12i5.pp4700-4711.

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In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of two thermal units, one nuclear unit and one hydro unit. The controller model is developed b

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Chien, Ting-Hsuan, Yu-Chuan Huang, and Yuan-Yih Hsu. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm." Energies 13, no. 20 (2020): 5320. http://dx.doi.org/10.3390/en13205320.

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An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a

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